https://media.trustradius.com/product-logos/zB/ny/E8P9M3TGXBRK.pngAnacondaAnaconda: The Data Science Starter Kit.2019-03-13T01:38:34.007ZAnaconda is being used for data analysis purposes. We use it to mainly manage python distributions and to preload scientific packages that make working with data very easy. It's used by pockets on campus, mostly those who have research needs. It relieves us from having to purchase expensive software like SAS or SPSS and uses both Python and R.,Clear install story. There are a lot of ways to install python. There's only one way to install anaconda. This makes teaching and standardizing much easier.
Batteries included. It's easy to install things in python, but anaconda ships with most of what you need out of the box. This helps with standardization and reproducibility.
Good integrations with Jupyter and other visual tools. Jupyter is really convenient when learning various python packages. Anaconda makes these tools easy to launch and to use.,Doesn't play well with other Python. I use python for more than data science, and whenever I have multiple versions of python on my machine —some using conda, some using Pipenv, some using poetry— it can get really confusing. If Anaconda is all you use, then it works really well.
Not all packages are available in Anaconda. Conda install doesn't always work for all PyPI packages. This adds to the frustration above - as you have to install some packages outside of conda, and then figure out how to use them internally.
Visualizations don't always work like you'd hope. This is getting better, but creating interactive graphics doesn't always work well in this context.,7,Anaconda has helped us analyze academic data to inform business decisions.
Anaconda has helped us visualize complex data sets into understandable graphics.
Anaconda has helped us explore machine learning abilities and limitations.,Microsoft Power BI,Power BI For Office 365, OneNote,15,2,Data Analytics. Evaluating data sets.
Data Exploration. Visualizing relationships within data sets.
Reporting. Creating reproducible reports.,Introduction to programming environment. It works well as an introduction, as the install story is really clear.
Data literacy. Helping non-programmers and non-statisticians better understand the capabilities of machine learning and other computing techniques.
Data pipelines. Moving data from one system to another is handled by many different systems - anaconda and python make this fairly easy.,Replacing programs such as Excel.
Generating interactive websites.
Automating reporting across the University.,7,Installation. This is the highlight of anaconda. It's really easy to install.
Launching interactive consoles. It integrates with Jupyter really well.
Getting started. All the libraries you'd need are included.,Managing Python virtual environments.
Version control. You can use git, but better integration would be nice.
Developing interactive websites. It's not really intended for this.,No,8Matthew Deakyne